基于PCI总线和DSP技术的生物芯片测控与采集系统

本文在介绍激光共聚焦生物芯片扫描仪结构原理基础上，论述了基于DSP的PC测控采集技术、基于PCI总线的PC与DSP通信技术等关键技术。     

计量秤在变角度状态的实践

介绍了物料计量秤在变角度下如何计算重量，以及变角度显示仪的工作原理和在实际中的运用。     

AD603在振动信号采集系统中的应用

本文介绍了ADI公司的电压控制增益低噪声、宽带放大器AD603的原理、特点及其在振动信号采集系统中应用的电路设计方法。     

双高速低噪声运算放大器AD8022

AD8022是ADI公司生产的双重高速低噪声运算放大器，它具有低噪声、低功耗，低失真等特点，可以作为各种数字用户线的接收放大器。文中介绍了AD8022的技术性能和工作原理，给出了用AD8022组成的差动输入Sallen-Key滤波器的应用电路。     

IMAGE仪器MRX站A/D板电路分析及故障排除

通过对IMAGE仪器MRX站A／D板的电路分析，利用综合测试和分析诊断程序，并借助示波器和万用表，针对不同的故障，找出A／D板产生故障的根源，切实有效地排除故障。     

绝缘电阻的数字测量

介绍如何利用AD674A来完成绝缘电阻的数字测量，阐述了硬件结构与软件设计。     

高精度模/数转换芯片AD7703与8515的接口

用两根口线实现了20位ADC的同步串行口数据采集，比其他方法大大节省了I／O口线，并且隔离方便。对口线资源紧缺的单片机和DSP系统具有很强的吸引力，有一定的推广价值。     

基于Mega8单片机和AD9833的正弦波信号发生器

正弦波信号作为一种基准信号广泛应用于各种电路中，产生正弦波信号的方法有很多种，DDS（Direct Digital Frequency Synthesis直接数字频率舍成器）作为一种新型技术，具有相对带宽宽，频率转换时间短，频率分辨率高，控制灵活方便等优点，成为现代频率合成技术中的佼佼者。AD公司生产的AD9833是一款低功耗、可编程波形发生器，在Mega8单片机的控制下可生成0-12．5MHz的正弦波，还可以生成三角波和方波。经现场验证，基于Mega8单片机和AD9833的正弦波信号发生器，可以非常方便地生成各种频率的正弦波。     

基于RTLinux和AD2S80A的角位置测量系统设计与实现

基于RTlinux和AD2S80A的角位置测量系统,选用旋转变压器和感应同步器作为测量系统的传感器,利用AD2S80A轴角变换器将两种测量传感器输出的模拟信号转换为相应的数字信号。根据双通道测角系统粗精耦合的原理,在RTLinux嵌入式系统中对两种数字信号进行耦合处理,并对耦合后信号的进行补偿处理,实现角位置的高精度测量。检测数据表明,这种测量系统可以实现以内的测量精度。     

A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.